# Kalman Filter module to correct ARIMA forecast result

I am currently writing a script to do a wind speed forecast using ARIMA and I have pretty nice results for a very short term forecast. I was wondering which of the Kalman Filter function in python is the one that would allow me to have lower root mean square error of my forecast. I am using only wind speed as input data and I want to see some improvements in my forecast errors. Can you give me some guidelines on which module to use, functions and be a little bit specific on why I can't use others. Do I have to use pykalman (kalman smoother)?Why?How? Or tsa.kalmanf.kalmanfilter (In this module I am seeing two different types of Kalman Filter: one with the k in capital and there is no comments on the function and the other in non capital letters) Any help would be great!

I would use this numpy implementation on the scipy wiki:

# Kalman filter example demo in Python

# A Python implementation of the example given in pages 11-15 of "An
# Introduction to the Kalman Filter" by Greg Welch and Gary Bishop,
# University of North Carolina at Chapel Hill, Department of Computer
# Science, TR 95-041,
# http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html

# by Andrew D. Straw

import numpy
import pylab

# intial parameters
n_iter = 50
sz = (n_iter,) # size of array
x = -0.37727 # truth value (typo in example at top of p. 13 calls this z)
z = numpy.random.normal(x,0.1,size=sz) # observations (normal about x, sigma=0.1)

Q = 1e-5 # process variance

# allocate space for arrays
xhat=numpy.zeros(sz)      # a posteri estimate of x
P=numpy.zeros(sz)         # a posteri error estimate
xhatminus=numpy.zeros(sz) # a priori estimate of x
Pminus=numpy.zeros(sz)    # a priori error estimate
K=numpy.zeros(sz)         # gain or blending factor

R = 0.1**2 # estimate of measurement variance, change to see effect

# intial guesses
xhat[0] = 0.0
P[0] = 1.0

for k in range(1,n_iter):
# time update
xhatminus[k] = xhat[k-1]
Pminus[k] = P[k-1]+Q

# measurement update
K[k] = Pminus[k]/( Pminus[k]+R )
xhat[k] = xhatminus[k]+K[k]*(z[k]-xhatminus[k])
P[k] = (1-K[k])*Pminus[k]

pylab.figure()
pylab.plot(z,'k+',label='noisy measurements')
pylab.plot(xhat,'b-',label='a posteri estimate')
pylab.axhline(x,color='g',label='truth value')
pylab.legend()
pylab.xlabel('Iteration')
pylab.ylabel('Voltage')

pylab.figure()
valid_iter = range(1,n_iter) # Pminus not valid at step 0
pylab.plot(valid_iter,Pminus[valid_iter],label='a priori error estimate')
pylab.xlabel('Iteration')
pylab.ylabel('\$(Voltage)^2\$')
pylab.setp(pylab.gca(),'ylim',[0,.01])
pylab.show()

A common misconception regarding the filter is that noise need be distributed normally. This is not required.